Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations16747
Missing cells5824
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory136.0 B

Variable types

Numeric10
Text3
Categorical2
DateTime1

Alerts

last_review has 2905 (17.3%) missing values Missing
reviews_per_month has 2905 (17.3%) missing values Missing
id has unique values Unique
number_of_reviews has 2905 (17.3%) zeros Zeros
availability_365 has 6635 (39.6%) zeros Zeros

Reproduction

Analysis started2025-04-25 09:23:31.205186
Analysis finished2025-04-25 09:23:47.935220
Duration16.73 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct16747
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18733164
Minimum2539
Maximum36485609
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size261.7 KiB
2025-04-25T17:23:48.076420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2539
5-th percentile1224161.5
Q19287529.5
median19317834
Q328616560
95-th percentile35234760
Maximum36485609
Range36483070
Interquartile range (IQR)19329031

Descriptive statistics

Standard deviation10903901
Coefficient of variation (CV)0.58206407
Kurtosis-1.2113291
Mean18733164
Median Absolute Deviation (MAD)9719442
Skewness-0.054980643
Sum3.1372429 × 1011
Variance1.1889507 × 1014
MonotonicityNot monotonic
2025-04-25T17:23:48.245539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9138664 1
 
< 0.1%
7885044 1
 
< 0.1%
20802860 1
 
< 0.1%
19387402 1
 
< 0.1%
32195520 1
 
< 0.1%
28978145 1
 
< 0.1%
28721018 1
 
< 0.1%
7308735 1
 
< 0.1%
35710560 1
 
< 0.1%
35084043 1
 
< 0.1%
Other values (16737) 16737
99.9%
ValueCountFrequency (%)
2539 1
< 0.1%
3831 1
< 0.1%
5022 1
< 0.1%
5203 1
< 0.1%
5238 1
< 0.1%
5803 1
< 0.1%
6848 1
< 0.1%
7750 1
< 0.1%
7801 1
< 0.1%
8024 1
< 0.1%
ValueCountFrequency (%)
36485609 1
< 0.1%
36485057 1
< 0.1%
36479723 1
< 0.1%
36478343 1
< 0.1%
36472171 1
< 0.1%
36471896 1
< 0.1%
36468880 1
< 0.1%
36458668 1
< 0.1%
36456829 1
< 0.1%
36455917 1
< 0.1%

name
Text

Distinct16566
Distinct (%)99.0%
Missing7
Missing (%)< 0.1%
Memory size261.7 KiB
2025-04-25T17:23:48.558430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length179
Median length56
Mean length36.461051
Min length1

Characters and Unicode

Total characters610358
Distinct characters489
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16436 ?
Unique (%)98.2%

Sample

1st rowPrivate Lg Room 15 min to Manhattan
2nd rowTIME SQUARE CHARMING ONE BED IN HELL'S KITCHEN,NYC
3rd rowVoted #1 Location Quintessential 1BR W Village Apt
4th rowSpacious 1 bedroom apartment 15min from Manhattan
5th rowBig beautiful bedroom in huge Bushwick apartment
ValueCountFrequency (%)
in 5955
 
5.9%
room 3661
 
3.6%
bedroom 2688
 
2.7%
private 2639
 
2.6%
2517
 
2.5%
apartment 2408
 
2.4%
cozy 1872
 
1.9%
apt 1535
 
1.5%
brooklyn 1508
 
1.5%
the 1393
 
1.4%
Other values (5970) 74697
74.1%
2025-04-25T17:23:49.023763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
84700
 
13.9%
o 42375
 
6.9%
e 42227
 
6.9%
t 36161
 
5.9%
a 35887
 
5.9%
r 33798
 
5.5%
i 32716
 
5.4%
n 32344
 
5.3%
l 17685
 
2.9%
m 17316
 
2.8%
Other values (479) 235149
38.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 610358
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
84700
 
13.9%
o 42375
 
6.9%
e 42227
 
6.9%
t 36161
 
5.9%
a 35887
 
5.9%
r 33798
 
5.5%
i 32716
 
5.4%
n 32344
 
5.3%
l 17685
 
2.9%
m 17316
 
2.8%
Other values (479) 235149
38.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 610358
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
84700
 
13.9%
o 42375
 
6.9%
e 42227
 
6.9%
t 36161
 
5.9%
a 35887
 
5.9%
r 33798
 
5.5%
i 32716
 
5.4%
n 32344
 
5.3%
l 17685
 
2.9%
m 17316
 
2.8%
Other values (479) 235149
38.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 610358
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
84700
 
13.9%
o 42375
 
6.9%
e 42227
 
6.9%
t 36161
 
5.9%
a 35887
 
5.9%
r 33798
 
5.5%
i 32716
 
5.4%
n 32344
 
5.3%
l 17685
 
2.9%
m 17316
 
2.8%
Other values (479) 235149
38.5%

host_id
Real number (ℝ)

Distinct15004
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66001283
Minimum2571
Maximum2.7427328 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size261.7 KiB
2025-04-25T17:23:49.192559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2571
5-th percentile773241.8
Q17748101
median30523475
Q31.019804 × 108
95-th percentile2.4064613 × 108
Maximum2.7427328 × 108
Range2.7427071 × 108
Interquartile range (IQR)94232295

Descriptive statistics

Standard deviation77701222
Coefficient of variation (CV)1.1772684
Kurtosis0.34747126
Mean66001283
Median Absolute Deviation (MAD)27039318
Skewness1.2651272
Sum1.1053235 × 1012
Variance6.03748 × 1015
MonotonicityNot monotonic
2025-04-25T17:23:49.344947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219517861 63
 
0.4%
119669058 17
 
0.1%
190921808 14
 
0.1%
417504 12
 
0.1%
213781715 10
 
0.1%
5144567 9
 
0.1%
224414117 8
 
< 0.1%
252604696 8
 
< 0.1%
201015598 8
 
< 0.1%
211549023 7
 
< 0.1%
Other values (14994) 16591
99.1%
ValueCountFrequency (%)
2571 1
 
< 0.1%
2787 3
< 0.1%
3151 1
 
< 0.1%
3415 1
 
< 0.1%
3563 1
 
< 0.1%
3647 2
< 0.1%
4396 1
 
< 0.1%
4869 1
 
< 0.1%
5089 1
 
< 0.1%
6041 1
 
< 0.1%
ValueCountFrequency (%)
274273284 1
< 0.1%
274195458 1
< 0.1%
274103383 1
< 0.1%
273870123 1
< 0.1%
273841667 1
< 0.1%
273741577 1
< 0.1%
273656890 1
< 0.1%
273619304 1
< 0.1%
273613106 1
< 0.1%
273570019 1
< 0.1%
Distinct5968
Distinct (%)35.7%
Missing7
Missing (%)< 0.1%
Memory size261.7 KiB
2025-04-25T17:23:49.600014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length35
Median length31
Mean length6.0646953
Min length1

Characters and Unicode

Total characters101523
Distinct characters134
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3921 ?
Unique (%)23.4%

Sample

1st rowIris
2nd rowJohlex
3rd rowJohn
4th rowRegan
5th rowMegan
ValueCountFrequency (%)
342
 
1.8%
and 208
 
1.1%
michael 151
 
0.8%
david 150
 
0.8%
john 121
 
0.7%
alex 107
 
0.6%
maria 99
 
0.5%
daniel 90
 
0.5%
sarah 87
 
0.5%
anna 80
 
0.4%
Other values (5596) 17067
92.2%
2025-04-25T17:23:50.084886image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 13092
 
12.9%
e 9896
 
9.7%
i 8409
 
8.3%
n 8272
 
8.1%
r 6041
 
6.0%
l 5251
 
5.2%
o 4246
 
4.2%
t 3209
 
3.2%
s 3186
 
3.1%
h 3164
 
3.1%
Other values (124) 36757
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 101523
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 13092
 
12.9%
e 9896
 
9.7%
i 8409
 
8.3%
n 8272
 
8.1%
r 6041
 
6.0%
l 5251
 
5.2%
o 4246
 
4.2%
t 3209
 
3.2%
s 3186
 
3.1%
h 3164
 
3.1%
Other values (124) 36757
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 101523
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 13092
 
12.9%
e 9896
 
9.7%
i 8409
 
8.3%
n 8272
 
8.1%
r 6041
 
6.0%
l 5251
 
5.2%
o 4246
 
4.2%
t 3209
 
3.2%
s 3186
 
3.1%
h 3164
 
3.1%
Other values (124) 36757
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 101523
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 13092
 
12.9%
e 9896
 
9.7%
i 8409
 
8.3%
n 8272
 
8.1%
r 6041
 
6.0%
l 5251
 
5.2%
o 4246
 
4.2%
t 3209
 
3.2%
s 3186
 
3.1%
h 3164
 
3.1%
Other values (124) 36757
36.2%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size261.7 KiB
Brooklyn
7334 
Manhattan
6715 
Queens
2135 
Bronx
 
413
Staten Island
 
150

Length

Max length13
Median length9
Mean length8.116797
Min length5

Characters and Unicode

Total characters135932
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQueens
2nd rowManhattan
3rd rowManhattan
4th rowQueens
5th rowBrooklyn

Common Values

ValueCountFrequency (%)
Brooklyn 7334
43.8%
Manhattan 6715
40.1%
Queens 2135
 
12.7%
Bronx 413
 
2.5%
Staten Island 150
 
0.9%

Length

2025-04-25T17:23:50.259480image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-25T17:23:50.423430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
brooklyn 7334
43.4%
manhattan 6715
39.7%
queens 2135
 
12.6%
bronx 413
 
2.4%
staten 150
 
0.9%
island 150
 
0.9%

Most occurring characters

ValueCountFrequency (%)
n 23612
17.4%
a 20445
15.0%
o 15081
11.1%
t 13730
10.1%
r 7747
 
5.7%
B 7747
 
5.7%
l 7484
 
5.5%
y 7334
 
5.4%
k 7334
 
5.4%
M 6715
 
4.9%
Other values (10) 18703
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 135932
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 23612
17.4%
a 20445
15.0%
o 15081
11.1%
t 13730
10.1%
r 7747
 
5.7%
B 7747
 
5.7%
l 7484
 
5.5%
y 7334
 
5.4%
k 7334
 
5.4%
M 6715
 
4.9%
Other values (10) 18703
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 135932
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 23612
17.4%
a 20445
15.0%
o 15081
11.1%
t 13730
10.1%
r 7747
 
5.7%
B 7747
 
5.7%
l 7484
 
5.5%
y 7334
 
5.4%
k 7334
 
5.4%
M 6715
 
4.9%
Other values (10) 18703
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 135932
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 23612
17.4%
a 20445
15.0%
o 15081
11.1%
t 13730
10.1%
r 7747
 
5.7%
B 7747
 
5.7%
l 7484
 
5.5%
y 7334
 
5.4%
k 7334
 
5.4%
M 6715
 
4.9%
Other values (10) 18703
13.8%
Distinct215
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size261.7 KiB
2025-04-25T17:23:50.613883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length26
Median length17
Mean length11.913358
Min length4

Characters and Unicode

Total characters199513
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)0.1%

Sample

1st rowSunnyside
2nd rowHell's Kitchen
3rd rowWest Village
4th rowAstoria
5th rowBushwick
ValueCountFrequency (%)
east 2237
 
8.4%
side 1432
 
5.3%
williamsburg 1407
 
5.3%
harlem 1385
 
5.2%
bedford-stuyvesant 1343
 
5.0%
heights 1320
 
4.9%
upper 1116
 
4.2%
village 1049
 
3.9%
bushwick 880
 
3.3%
west 856
 
3.2%
Other values (227) 13760
51.4%
2025-04-25T17:23:50.953223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 18133
 
9.1%
i 14026
 
7.0%
s 13940
 
7.0%
a 13151
 
6.6%
t 13140
 
6.6%
l 11714
 
5.9%
r 11707
 
5.9%
10038
 
5.0%
n 9039
 
4.5%
o 8443
 
4.2%
Other values (44) 76182
38.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 199513
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 18133
 
9.1%
i 14026
 
7.0%
s 13940
 
7.0%
a 13151
 
6.6%
t 13140
 
6.6%
l 11714
 
5.9%
r 11707
 
5.9%
10038
 
5.0%
n 9039
 
4.5%
o 8443
 
4.2%
Other values (44) 76182
38.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 199513
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 18133
 
9.1%
i 14026
 
7.0%
s 13940
 
7.0%
a 13151
 
6.6%
t 13140
 
6.6%
l 11714
 
5.9%
r 11707
 
5.9%
10038
 
5.0%
n 9039
 
4.5%
o 8443
 
4.2%
Other values (44) 76182
38.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 199513
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 18133
 
9.1%
i 14026
 
7.0%
s 13940
 
7.0%
a 13151
 
6.6%
t 13140
 
6.6%
l 11714
 
5.9%
r 11707
 
5.9%
10038
 
5.0%
n 9039
 
4.5%
o 8443
 
4.2%
Other values (44) 76182
38.2%

latitude
Real number (ℝ)

Distinct11179
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.727028
Minimum40.50873
Maximum40.91306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size261.7 KiB
2025-04-25T17:23:51.154393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum40.50873
5-th percentile40.643146
Q140.687595
median40.72007
Q340.76298
95-th percentile40.827477
Maximum40.91306
Range0.40433
Interquartile range (IQR)0.075385

Descriptive statistics

Standard deviation0.056214402
Coefficient of variation (CV)0.0013802726
Kurtosis0.035075335
Mean40.727028
Median Absolute Deviation (MAD)0.03631
Skewness0.28256433
Sum682055.54
Variance0.003160059
MonotonicityNot monotonic
2025-04-25T17:23:51.335501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.68084 7
 
< 0.1%
40.71813 7
 
< 0.1%
40.68634 7
 
< 0.1%
40.68683 7
 
< 0.1%
40.72232 7
 
< 0.1%
40.69054 6
 
< 0.1%
40.77127 6
 
< 0.1%
40.72049 6
 
< 0.1%
40.72607 6
 
< 0.1%
40.68592 6
 
< 0.1%
Other values (11169) 16682
99.6%
ValueCountFrequency (%)
40.50873 1
< 0.1%
40.52293 1
< 0.1%
40.53871 1
< 0.1%
40.53939 1
< 0.1%
40.54106 1
< 0.1%
40.54312 1
< 0.1%
40.5455 1
< 0.1%
40.54889 1
< 0.1%
40.54901 1
< 0.1%
40.55182 1
< 0.1%
ValueCountFrequency (%)
40.91306 1
< 0.1%
40.90391 1
< 0.1%
40.90356 1
< 0.1%
40.90329 1
< 0.1%
40.90281 1
< 0.1%
40.9026 1
< 0.1%
40.89981 1
< 0.1%
40.89811 1
< 0.1%
40.89756 1
< 0.1%
40.89747 1
< 0.1%

longitude
Real number (ℝ)

Distinct9323
Distinct (%)55.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.949359
Minimum-74.23914
Maximum-73.71795
Zeros0
Zeros (%)0.0%
Negative16747
Negative (%)100.0%
Memory size261.7 KiB
2025-04-25T17:23:51.757673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-74.23914
5-th percentile-74.00315
Q1-73.98123
median-73.95365
Q3-73.932665
95-th percentile-73.855294
Maximum-73.71795
Range0.52119
Interquartile range (IQR)0.048565

Descriptive statistics

Standard deviation0.047919729
Coefficient of variation (CV)-0.00064800737
Kurtosis4.5825772
Mean-73.949359
Median Absolute Deviation (MAD)0.02504
Skewness1.2193676
Sum-1238429.9
Variance0.0022963004
MonotonicityNot monotonic
2025-04-25T17:23:51.923844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.98589 9
 
0.1%
-73.94829 8
 
< 0.1%
-73.95443 8
 
< 0.1%
-73.95121 8
 
< 0.1%
-73.95332 8
 
< 0.1%
-73.95107 8
 
< 0.1%
-73.95427 7
 
< 0.1%
-73.95688 7
 
< 0.1%
-73.95471 7
 
< 0.1%
-73.95476 7
 
< 0.1%
Other values (9313) 16670
99.5%
ValueCountFrequency (%)
-74.23914 1
< 0.1%
-74.21238 1
< 0.1%
-74.19626 1
< 0.1%
-74.18259 1
< 0.1%
-74.17388 1
< 0.1%
-74.17117 1
< 0.1%
-74.17065 1
< 0.1%
-74.16966 1
< 0.1%
-74.16634 1
< 0.1%
-74.16558 1
< 0.1%
ValueCountFrequency (%)
-73.71795 1
< 0.1%
-73.71829 1
< 0.1%
-73.72582 1
< 0.1%
-73.72716 1
< 0.1%
-73.72731 1
< 0.1%
-73.7274 1
< 0.1%
-73.72778 1
< 0.1%
-73.72817 1
< 0.1%
-73.72901 1
< 0.1%
-73.72928 1
< 0.1%

room_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size261.7 KiB
Private room
8316 
Entire home/apt
8041 
Shared room
 
390

Length

Max length15
Median length12
Mean length13.417149
Min length11

Characters and Unicode

Total characters224697
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowPrivate room

Common Values

ValueCountFrequency (%)
Private room 8316
49.7%
Entire home/apt 8041
48.0%
Shared room 390
 
2.3%

Length

2025-04-25T17:23:52.137258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-25T17:23:52.340630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
room 8706
26.0%
private 8316
24.8%
entire 8041
24.0%
home/apt 8041
24.0%
shared 390
 
1.2%

Most occurring characters

ValueCountFrequency (%)
o 25453
11.3%
r 25453
11.3%
e 24788
11.0%
t 24398
10.9%
m 16747
7.5%
a 16747
7.5%
16747
7.5%
i 16357
 
7.3%
h 8431
 
3.8%
P 8316
 
3.7%
Other values (7) 41260
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 224697
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 25453
11.3%
r 25453
11.3%
e 24788
11.0%
t 24398
10.9%
m 16747
7.5%
a 16747
7.5%
16747
7.5%
i 16357
 
7.3%
h 8431
 
3.8%
P 8316
 
3.7%
Other values (7) 41260
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 224697
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 25453
11.3%
r 25453
11.3%
e 24788
11.0%
t 24398
10.9%
m 16747
7.5%
a 16747
7.5%
16747
7.5%
i 16357
 
7.3%
h 8431
 
3.8%
P 8316
 
3.7%
Other values (7) 41260
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 224697
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 25453
11.3%
r 25453
11.3%
e 24788
11.0%
t 24398
10.9%
m 16747
7.5%
a 16747
7.5%
16747
7.5%
i 16357
 
7.3%
h 8431
 
3.8%
P 8316
 
3.7%
Other values (7) 41260
18.4%

price
Real number (ℝ)

Distinct302
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.08264
Minimum10
Maximum350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size261.7 KiB
2025-04-25T17:23:52.502589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile40
Q167
median100
Q3159
95-th percentile265
Maximum350
Range340
Interquartile range (IQR)92

Descriptive statistics

Standard deviation70.664779
Coefficient of variation (CV)0.58360784
Kurtosis0.64243345
Mean121.08264
Median Absolute Deviation (MAD)41
Skewness1.0754008
Sum2027771
Variance4993.511
MonotonicityNot monotonic
2025-04-25T17:23:52.686148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 783
 
4.7%
150 725
 
4.3%
50 582
 
3.5%
75 538
 
3.2%
200 528
 
3.2%
60 511
 
3.1%
80 493
 
2.9%
70 441
 
2.6%
65 438
 
2.6%
120 430
 
2.6%
Other values (292) 11278
67.3%
ValueCountFrequency (%)
10 6
< 0.1%
11 2
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
15 1
 
< 0.1%
16 3
 
< 0.1%
18 1
 
< 0.1%
19 2
 
< 0.1%
20 12
0.1%
21 3
 
< 0.1%
ValueCountFrequency (%)
350 132
0.8%
349 14
 
0.1%
345 11
 
0.1%
344 1
 
< 0.1%
342 1
 
< 0.1%
341 1
 
< 0.1%
340 8
 
< 0.1%
339 4
 
< 0.1%
336 1
 
< 0.1%
335 3
 
< 0.1%

minimum_nights
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.860572
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size261.7 KiB
2025-04-25T17:23:52.830727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum14
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.291717
Coefficient of variation (CV)0.80113941
Kurtosis7.0373867
Mean2.860572
Median Absolute Deviation (MAD)1
Skewness2.3206945
Sum47906
Variance5.2519668
MonotonicityNot monotonic
2025-04-25T17:23:52.978011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 5003
29.9%
2 4619
27.6%
3 3086
18.4%
4 1253
 
7.5%
5 1140
 
6.8%
7 822
 
4.9%
6 285
 
1.7%
14 210
 
1.3%
10 178
 
1.1%
8 52
 
0.3%
Other values (4) 99
 
0.6%
ValueCountFrequency (%)
1 5003
29.9%
2 4619
27.6%
3 3086
18.4%
4 1253
 
7.5%
5 1140
 
6.8%
6 285
 
1.7%
7 822
 
4.9%
8 52
 
0.3%
9 34
 
0.2%
10 178
 
1.1%
ValueCountFrequency (%)
14 210
 
1.3%
13 20
 
0.1%
12 34
 
0.2%
11 11
 
0.1%
10 178
 
1.1%
9 34
 
0.2%
8 52
 
0.3%
7 822
4.9%
6 285
 
1.7%
5 1140
6.8%

number_of_reviews
Real number (ℝ)

Zeros 

Distinct320
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.856392
Minimum0
Maximum607
Zeros2905
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size261.7 KiB
2025-04-25T17:23:53.127915image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q327
95-th percentile123
Maximum607
Range607
Interquartile range (IQR)26

Descriptive statistics

Standard deviation47.30526
Coefficient of variation (CV)1.8295383
Kurtosis17.615739
Mean25.856392
Median Absolute Deviation (MAD)7
Skewness3.5194961
Sum433017
Variance2237.7876
MonotonicityNot monotonic
2025-04-25T17:23:53.287414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2905
17.3%
1 1718
 
10.3%
2 1160
 
6.9%
3 844
 
5.0%
4 692
 
4.1%
5 521
 
3.1%
6 499
 
3.0%
7 439
 
2.6%
8 407
 
2.4%
9 345
 
2.1%
Other values (310) 7217
43.1%
ValueCountFrequency (%)
0 2905
17.3%
1 1718
10.3%
2 1160
 
6.9%
3 844
 
5.0%
4 692
 
4.1%
5 521
 
3.1%
6 499
 
3.0%
7 439
 
2.6%
8 407
 
2.4%
9 345
 
2.1%
ValueCountFrequency (%)
607 1
< 0.1%
594 1
< 0.1%
510 1
< 0.1%
488 1
< 0.1%
474 1
< 0.1%
466 1
< 0.1%
459 1
< 0.1%
448 1
< 0.1%
441 1
< 0.1%
439 1
< 0.1%

last_review
Date

Missing 

Distinct1444
Distinct (%)10.4%
Missing2905
Missing (%)17.3%
Memory size261.7 KiB
Minimum2011-05-12 00:00:00
Maximum2019-07-08 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-25T17:23:53.447479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:53.643993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

Missing 

Distinct789
Distinct (%)5.7%
Missing2905
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean1.4697789
Minimum0.01
Maximum27.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size261.7 KiB
2025-04-25T17:23:53.792976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.04
Q10.21
median0.83
Q32.18
95-th percentile4.8195
Maximum27.95
Range27.94
Interquartile range (IQR)1.97

Descriptive statistics

Standard deviation1.7342197
Coefficient of variation (CV)1.1799187
Kurtosis11.316384
Mean1.4697789
Median Absolute Deviation (MAD)0.72
Skewness2.3434648
Sum20344.68
Variance3.007518
MonotonicityNot monotonic
2025-04-25T17:23:53.956071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 344
 
2.1%
1 316
 
1.9%
0.05 296
 
1.8%
0.03 293
 
1.7%
0.08 227
 
1.4%
0.04 225
 
1.3%
0.16 216
 
1.3%
0.09 194
 
1.2%
0.06 189
 
1.1%
0.11 180
 
1.1%
Other values (779) 11362
67.8%
(Missing) 2905
 
17.3%
ValueCountFrequency (%)
0.01 13
 
0.1%
0.02 344
2.1%
0.03 293
1.7%
0.04 225
1.3%
0.05 296
1.8%
0.06 189
1.1%
0.07 135
 
0.8%
0.08 227
1.4%
0.09 194
1.2%
0.1 155
0.9%
ValueCountFrequency (%)
27.95 1
< 0.1%
20.94 1
< 0.1%
19.75 1
< 0.1%
17.82 1
< 0.1%
16.22 1
< 0.1%
13.45 1
< 0.1%
13.42 1
< 0.1%
13.24 1
< 0.1%
13.15 1
< 0.1%
12.99 1
< 0.1%
Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1051532
Minimum1
Maximum327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size261.7 KiB
2025-04-25T17:23:54.111090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum327
Range326
Interquartile range (IQR)1

Descriptive statistics

Standard deviation20.105021
Coefficient of variation (CV)6.4747276
Kurtosis250.53754
Mean3.1051532
Median Absolute Deviation (MAD)0
Skewness15.751848
Sum52002
Variance404.21186
MonotonicityNot monotonic
2025-04-25T17:23:54.255385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 11762
70.2%
2 2397
 
14.3%
3 1024
 
6.1%
4 469
 
2.8%
5 290
 
1.7%
6 157
 
0.9%
7 132
 
0.8%
8 115
 
0.7%
9 65
 
0.4%
327 63
 
0.4%
Other values (15) 273
 
1.6%
ValueCountFrequency (%)
1 11762
70.2%
2 2397
 
14.3%
3 1024
 
6.1%
4 469
 
2.8%
5 290
 
1.7%
6 157
 
0.9%
7 132
 
0.8%
8 115
 
0.7%
9 65
 
0.4%
10 51
 
0.3%
ValueCountFrequency (%)
327 63
0.4%
47 14
 
0.1%
43 2
 
< 0.1%
34 17
 
0.1%
33 10
 
0.1%
30 8
 
< 0.1%
28 17
 
0.1%
26 5
 
< 0.1%
20 8
 
< 0.1%
18 7
 
< 0.1%

availability_365
Real number (ℝ)

Zeros 

Distinct366
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.520571
Minimum0
Maximum365
Zeros6635
Zeros (%)39.6%
Negative0
Negative (%)0.0%
Memory size261.7 KiB
2025-04-25T17:23:54.438446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q3179
95-th percentile353
Maximum365
Range365
Interquartile range (IQR)179

Descriptive statistics

Standard deviation124.83891
Coefficient of variation (CV)1.280129
Kurtosis-0.54187871
Mean97.520571
Median Absolute Deviation (MAD)25
Skewness0.9897628
Sum1633177
Variance15584.753
MonotonicityNot monotonic
2025-04-25T17:23:54.614688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6635
39.6%
365 300
 
1.8%
1 149
 
0.9%
364 132
 
0.8%
5 124
 
0.7%
3 107
 
0.6%
6 105
 
0.6%
2 104
 
0.6%
179 102
 
0.6%
4 102
 
0.6%
Other values (356) 8887
53.1%
ValueCountFrequency (%)
0 6635
39.6%
1 149
 
0.9%
2 104
 
0.6%
3 107
 
0.6%
4 102
 
0.6%
5 124
 
0.7%
6 105
 
0.6%
7 87
 
0.5%
8 82
 
0.5%
9 81
 
0.5%
ValueCountFrequency (%)
365 300
1.8%
364 132
0.8%
363 74
 
0.4%
362 57
 
0.3%
361 36
 
0.2%
360 27
 
0.2%
359 48
 
0.3%
358 43
 
0.3%
357 24
 
0.1%
356 23
 
0.1%

Interactions

2025-04-25T17:23:45.933657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:33.607628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:34.990227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:36.222848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:37.565963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:39.068119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:40.289768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:41.523380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:42.721281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:44.156249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:46.060197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:33.759961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:35.099036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:36.344214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:37.713344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:39.186655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:40.429443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:41.648986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:42.826067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:44.323378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:46.205536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:33.886448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:35.252527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:36.474036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:37.829915image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:39.302229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:40.564075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:41.762402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:42.937670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:44.433000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:46.329426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:34.031832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:35.368087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:36.603490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:38.008074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:39.436720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:40.707103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:41.892844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:43.099813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:44.568920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:46.452249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:34.146281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:35.499770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:36.758693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:38.119377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:39.567197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:40.817391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:42.003942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:43.228138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:44.693190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:46.596400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:34.302120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:35.616350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:36.892651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:38.253707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:39.693738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:40.928926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:42.128510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:43.380874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:44.855153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:46.723913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:34.480977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:35.743606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:37.012112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:38.368270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:39.813749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:41.035948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:42.268503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:43.531298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:44.973426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:46.852354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:34.619378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:35.850442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:37.146308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:38.488710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:39.927228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:41.175468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:42.391538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:43.718321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:45.123769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:46.969269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:34.744402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:35.971335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:37.269341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:38.815246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:40.035321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:41.289434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:42.505919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:43.836691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:45.273017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:47.090021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:34.855121image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:36.088450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:37.414517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:38.943264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:40.158081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:41.396699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:42.614067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:43.953168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-04-25T17:23:45.769343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2025-04-25T17:23:47.289325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-25T17:23:47.616284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-25T17:23:47.841659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
09138664Private Lg Room 15 min to Manhattan47594947IrisQueensSunnyside40.74271-73.92493Private room74262019-05-260.1315
131444015TIME SQUARE CHARMING ONE BED IN HELL'S KITCHEN,NYC8523790JohlexManhattanHell's Kitchen40.76682-73.98878Entire home/apt17030NaTNaN1188
28741020Voted #1 Location Quintessential 1BR W Village Apt45854238JohnManhattanWest Village40.73631-74.00611Entire home/apt2453512018-09-191.1210
334602077Spacious 1 bedroom apartment 15min from Manhattan261055465ReganQueensAstoria40.76424-73.92351Entire home/apt125312019-05-240.65113
423203149Big beautiful bedroom in huge Bushwick apartment143460MeganBrooklynBushwick40.69839-73.92044Private room65282019-06-230.5228
54402805LRG 2br BKLYN APT CLOSE TO TRAINS AND PARK22807362JennyBrooklynProspect-Lefferts Gardens40.66025-73.96270Entire home/apt120332018-08-280.05116
630070126✩Prime Renovated 1/1 Apartment in Upper East Side✩4968673SeanManhattanUpper East Side40.76831-73.95929Entire home/apt200522019-05-260.68171
734231172Fully renovated brick house floor in Brooklyn59642348KevinBrooklynSunset Park40.64550-74.01262Entire home/apt95192019-07-089.001106
85856760Renovated 1BR in exciting, convenient area29408349ChadManhattanChinatown40.71490-73.99976Entire home/apt179572017-04-180.1410
97929441Beautiful Loft w/ Waterfront View!1453898AnthonyBrooklynWilliamsburg40.71268-73.96676Private room10522322019-06-195.00364
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
199905192459Quiet Room in 4BR UWS Brownstone10677483GregManhattanUpper West Side40.80173-73.96625Private room7010NaTNaN10
199911327940Huge Gorgeous Park View Apartment!3290436HadarBrooklynFlatbush40.65335-73.96257Entire home/apt1203132016-08-270.282327
1999223612681Shared Room 1 Stop from Manhattan on the F Train55724558TaylorQueensLong Island City40.76006-73.94080Private room55422019-06-010.65589
1999334485745Midtown Manhattan Stunner - Private room261632622RoyaltonManhattanTheater District40.75491-73.98507Private room100132019-06-163.009318
1999425616250Stylish, spacious, private 1BR apt in Ditmas Park125396920AdamBrooklynFlatbush40.64314-73.95705Entire home/apt753102019-01-030.8410
199957094539Tranquil haven in bubbly Brooklyn2052211AdrianaBrooklynWindsor Terrace40.65360-73.97546Entire home/apt1431422016-08-270.04110
199964424261Large 1 BR with backyard on UWS3447311SarahManhattanUpper West Side40.80188-73.96808Entire home/apt2002222019-05-210.5010
199974545882Amazing studio/Loft with a backyard23569951KavehManhattanUpper East Side40.78110-73.94567Entire home/apt2203282019-05-230.501293
1999826518547U2 comfortable double bed sleeps 2 guests295128Carol GloriaBronxClason Point40.81225-73.85502Private room80142019-07-011.487365
1999933631782Private Bedroom in Williamsburg Apt!8569221AndiBrooklynWilliamsburg40.71829-73.95819Private room109332019-04-281.07297